Key Responsibilities:

Design, build, and operate scalable batch and real-time data pipelines in Databricks and related tooling to support audience, personalization, and measurement use cases. Build and maintain data models, feature/attribute pipelines, and marketing data feeds to decisioning systems and measurement platforms. Implement data-quality validation, monitoring, and change management so segments, flags, and attributes are accurate and trustworthy. Deliver governed customer attributes and audiences to activation platforms (e.g., Hightouch, Salesforce Marketing Cloud) with clear ownership and documentation. Partner with data science to productionize model features and with architecture to align on data contracts and identity resolution. Support privacy and compliance requirements across all data flows. Provide leadership-ready status, roadmap, and metrics reporting, and prepare decision memos that drive cross-team and leadership alignment.

Basic Qualifications:

3+ years of experience in a data engineering role required . Strong SQL and proficiency in a programming language for data engineering (e.g., Python or Scala). Hands-on experience with Databricks/Spark, data lake/warehouse design, and pipeline orchestration. Experience with real-time/streaming data (e.g., Kafka) and event-driven architectures. Experience with reverse-ETL/activation and identity resolution (e.g., Hightouch, LiveRamp/UID2) preferred. Understanding of data modeling, data quality, governance, and change management. Familiarity with marketing/customer data concepts (C360, RFM, LTV, loyalty) and compliant handling of PII.

Extra Credit:

Experience in a MarTech, digital marketing, personalization, CRM, or retail-media ecosystem. Familiarity with platforms in our stack, e.g., Salesforce Marketing Cloud (SFMC), Databricks, Hightouch, Kafka, Contentstack / CMS & DAM, LiveRamp / UID2. Retail or convenience-retail industry experience.